ABSTRACT Mechanical ventilation refers to the use of life-support technology to perform the work of breathing for patients suffering from respiratory failure. Patients undergoing mechanical ventilation are disproportionately older and suffer from multiple chronic conditions: Approximately half of these patients are older than 65, and half suffer from multiple chronic conditions. Prolonged mechanical ventilation is associated with a higher likelihood of death as a result of complications from ventilator associated conditions (VAC), the most lethal of which is ventilator associated pneumonia (VAP). Approximately 10 to 20% of mechanically ventilated patients develop VAP, and patients suffering from VAP are twice as likely to die compared to similar patients without VAP. In addition, approximately 80% of mechanically ventilated patients will develop delirium. Currently, most institutions take a one-size-fits-all ?bundled? approach to mitigate ventilator associated complications. This wastes healthcare resources on patients who will not benefit while simultaneously denying additional potentially life-saving resources from patients who are most likely to benefit from vigorous prophylactic interventions. In this Phase 1 SBIR study, we will design models to predict with a high degree of accuracy which patients will likely develop VAC, VAP and delirium. Current care focuses on the disease (i.e., respiratory failure) as opposed to the patient. Our vision is to put this tool into the hands of hospital caregivers, which we will do during Phase 2 of this SBIR. Successful completion of the proposed work will alter the current bundled approach to the care of mechanically ventilated patients such that the care becomes tailored to the needs of each individual patient. Furthermore, this work will facilitate the early application of targeted prevention interventions to reduce the frequency of VAC, pneumonia and delirium in mechanically ventilated patients, thus improving patient outcomes. Finally, the developed models will provide critical prognostic information for providers and patients, facilitating shared decision-making and care planning.